5.25 GB | 20min 21s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
1 -Understand the MLOps Project you will Build in the Course.mp4 (31.92 MB)
1 -What is MLOps.mp4 (353.71 MB)
2 -Story of Evolution of MLOps, LLMOps and AgenticAIOps.mp4 (308.4 MB)
3 -Comparing Three Approaches to AI.mp4 (350.94 MB)
4 -MLOps Case Studies - Learning from the Pioneers.mp4 (123.99 MB)
5 -Comparing Devops vs MLOps.mp4 (287.07 MB)
6 -Emergence of MLOps Engineer.mp4 (229.53 MB)
1 -Module Intro.mp4 (65.98 MB)
10 -Summary.mp4 (74.64 MB)
2 -Use Case - House Price Predictor - Regression.mp4 (31.92 MB)
3 -Understanding End to End ML Practices and MLOps.mp4 (81.28 MB)
4 -Environment Setup Overview.mp4 (68.07 MB)
5 -Setting up Docker Podman with Compose.mp4 (36.55 MB)
6 -Launching MLflow for Experiemnt Tracking.mp4 (57.25 MB)
7 -Understanding the Project Directory and Scaffold.mp4 (66.77 MB)
8 -Setting up Python Virtual Environment with UV.mp4 (42.88 MB)
9 -Working with Jupyter Notebooks.mp4 (68.44 MB)
1 -Module Intro.mp4 (66.13 MB)
10 -Module Summary.mp4 (70.57 MB)
2 -Learning Data Engineering.mp4 (117.28 MB)
3 -Experimental Data Analysis.mp4 (82.09 MB)
4 -Understaing Feature Engineering Concepts.mp4 (28.18 MB)
5 -Building New Features for House Price Predictor.mp4 (59.78 MB)
6 -Preparing for Model Experimentation.mp4 (56.02 MB)
7 -Data Splitting with x train, y train, x test, y test.mp4 (50.99 MB)
8 -Defining Algorithms and Hyperparameter Grids.mp4 (62.31 MB)
9 -Running Model Experiments to find the Best Model and Hyperparamters.mp4 (105.99 MB)
1 -Module Intro.mp4 (16.36 MB)
2 -Linear Regression.mp4 (50.35 MB)
3 -Logistic Regression.mp4 (26.64 MB)
4 -Decision Tree.mp4 (28.22 MB)
5 -Random Forest.mp4 (39.32 MB)
6 -Support Vector Machine (SVM).mp4 (25.33 MB)
7 -Neural Networking.mp4 (45.23 MB)
8 -Boosting Algorithms (XGBoost, LightGBM etc ).mp4 (84.57 MB)
9 -Module Summary.mp4 (29.58 MB)
1 -Module Intro.mp4 (65.75 MB)
10 -Summary.mp4 (85.8 MB)
2 -Handover from Data Scientist to ML Engineer MLOps.mp4 (30.43 MB)
3 -Running Feature Engineering and Preprocessing Jobs.mp4 (44.79 MB)
4 -Building and Training Final Model with Configs from Data Scientists.mp4 (52.1 MB)
5 -Wrapping the Model with FastAPI with Streamlit Client Apps.mp4 (77.62 MB)
6 -Writing Dockerfile to package Model with FastAPI Wrapper.mp4 (116.67 MB)
7 -Debugging and Fixing Image Failures, Launch and Validate FastAPI.mp4 (96.51 MB)
8 -Packaging and testing Streamlit App.mp4 (67.75 MB)
9 -Packaging and Model Serving Infra with Docker Compose.mp4 (102.73 MB)
1 -Moule Intro.mp4 (58.04 MB)
10 -Summary.mp4 (54 MB)
2 -DAGs, GitHub Actions and our MLOps CI Workflow.mp4 (53.21 MB)
3 -Understanding GitHub Actions Syntax.mp4 (57.65 MB)
4 -Writing an executung out first GitHub Actions Workflow.mp4 (118.71 MB)
5 -Adding Data and Feature Engineering Steps with Model Training.mp4 (56.55 MB)
6 -Model Training Step with MLFlow for Tracking.mp4 (79.43 MB)
7 -Adding Image Build and Publish Step with Docker.mp4 (53.44 MB)
8 -Configurating Registry Token and publishing Image to DockerHub.mp4 (65.19 MB)
9 -Modular, Multi Stage MLOps CI Workflow Pipeline.mp4 (151.95 MB)
1 -Module Intro.mp4 (41.98 MB)
10 -Easy way to Generate Kubernetes Manifets and YAML.mp4 (31.73 MB)
11 -Summary.mp4 (41.5 MB)
2 -Designing Scalable Infrastructure for Model Inference.mp4 (18.52 MB)
3 -Introduction to Kubernetes for Machine Learning.mp4 (47.41 MB)
4 -Kubernetes Core Concepts - Pods, Deployments and Services.mp4 (39.58 MB)
5 -Simplest way to build a 3 Node Kubernetes Cluster with KIND.mp4 (70.82 MB)
6 -Deploying Streamlit Frontent App with Kubernetes.mp4 (89.83 MB)
7 -Exposing the Streamlit App with Kubernetes NodePort Service.mp4 (44.41 MB)
8 -Creating Deployment Service for the Model wrapped in FastAPI.mp4 (47.69 MB)
9 -Connecting Streamlit with Model using Kubernetes native DNS Based Service Discov.mp4 (86.65 MB)]
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